{"id":96550,"date":"2021-06-04T12:21:17","date_gmt":"2021-06-04T06:51:17","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=96550"},"modified":"2026-06-01T14:35:50","modified_gmt":"2026-06-01T09:05:50","slug":"emoji-prediction-deep-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/emoji-prediction-deep-learning\/","title":{"rendered":"Emoji Prediction using Deep Learning"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:2638,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1eXbsb87bHO_j6AEhP-lCx61lfSiwkEP3\\\/view?usp=drive_link&quot;,&quot;archived_href&quot;:&quot;&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[],&quot;broken&quot;:false,&quot;last_checked&quot;:null,&quot;process&quot;:&quot;done&quot;},{&quot;id&quot;:361,&quot;href&quot;:&quot;https:\\\/\\\/www.kaggle.com\\\/watts2\\\/glove6b50dtxt&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20230106090455\\\/https:\\\/\\\/www.kaggle.com\\\/watts2\\\/glove6b50dtxt&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-08 09:04:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-11 09:11:00&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-14 21:39:09&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-18 02:37:32&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-21 13:04:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-27 13:29:16&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-01 11:35:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-06 13:37:44&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-10 12:53:42&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-15 07:04:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-22 18:41:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-28 17:41:04&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-05 08:43:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-08 10:50:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-17 05:26:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-23 08:02:52&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-27 05:51:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-04 18:34:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-13 13:43:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-22 03:29:02&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-27 11:46:00&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-02 18:03:47&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-14 07:41:53&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-17 19:03:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-21 10:32:17&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-26 19:32:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-05 10:21:01&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-10 16:42:18&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-15 15:43:07&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-20 03:37:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-25 17:35:55&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-02 09:01:27&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-02 09:01:27&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>Emojis are a wonderful method to express oneself.<\/p>\n<p>This deep learning project automatically predicts emojis based on a given phrase.<\/p>\n<h3>About Emoji Prediction Project<\/h3>\n<p>In this machine learning project, we predict the emoji from the given text. This means we build a text classifier that returns an emoji that suits the given text.<\/p>\n<p>Our systems should be aware of the relevant emoji to use at the proper moment.<\/p>\n<h3>Emoji Prediciton Dataset<\/h3>\n<p>The dataset consists of 2 parts, each is used for training and testing the deep learning model.<\/p>\n<p>The training dataset contains 4 columns, one column being the text and the other contains IDs representing the emojis. Keep in mind that, here in our dataset the same sentence can have more than 1 emoji as a result.<\/p>\n<p>You can download the emoji prediction dataset along with the project code in the next section.<\/p>\n<h3>Tools and Libraries:<\/h3>\n<ul>\n<li>Python &#8211; 3.x<\/li>\n<li>Numpy &#8211; 1.19.2<\/li>\n<li>Pandas &#8211; 1.2.4<\/li>\n<li>TensorFlow &#8211; 2.4.x<\/li>\n<li>Emoji &#8211; 1.2.0<\/li>\n<\/ul>\n<p>To install the above modules, run the following command:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">pip install numpy pandas tensorflow emoji<\/pre>\n<h3>Emoji Prediction Project Code &amp; Dataset<\/h3>\n<p>Please download the dataset &amp; source code of the emoji prediction project (which is explained below): <a href=\"https:\/\/drive.google.com\/file\/d\/1eXbsb87bHO_j6AEhP-lCx61lfSiwkEP3\/view?usp=drive_link\"><strong>Emoji Prediction Python Code &amp; Dataset<\/strong><\/a><\/p>\n<h3>Steps to build Emoji Prediction model<\/h3>\n<p>To build this text classifier, we follow the below steps:<\/p>\n<p>1. Perform Exploratory Data Analysis (EDA).<br \/>\n2. Build the classifier model.<br \/>\n3. Train and evaluate the model.<\/p>\n<h4>Step 1: Perform Exploratory Data Analysis (EDA)<\/h4>\n<p>Load the dataset using pandas.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\n\r\ntrain = pd.read_csv('.\/Desktop\/DataFlair\/train_emoji.csv',header=None)\r\ntest = pd.read_csv('.\/Desktop\/DataFlair\/test_emoji.csv',header=None)\r\n<\/pre>\n<p>Now, let&#8217;s have a look at the datasets.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">train.head()<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96637\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head.png\" alt=\"train head\" width=\"1200\" height=\"207\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head-300x52.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head-1024x177.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head-150x26.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head-768x132.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head-720x124.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head-520x90.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/train-head-320x55.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">test.head()<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96638\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head.png\" alt=\"test head\" width=\"1200\" height=\"214\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head-300x54.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head-1024x183.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head-150x27.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head-768x137.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head-720x128.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head-520x93.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/test-head-320x57.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>If you observe, there are 5 types of emojis in our dataset: heart, baseball, smile, disappointed, fork and knife.<\/p>\n<p>Let&#8217;s store the above information in a dictionary for ease of use.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">emoji_dict = { 0 : \":heart:\", 1 : \":baseball:\", 2 : \":smile:\", 3 : \":disappointed:\", 4 : \":fork_and_knife:\"}<\/pre>\n<p>Now using the emoji module, see how these emojis turn out!<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import emoji\r\n\r\nfor ix in emoji_dict.keys():\r\n    print (ix,end=\" \")\r\n    print (emoji.emojize(emoji_dict[ix], use_aliases=True))\r\n<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96639\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji.png\" alt=\"emoji\" width=\"1200\" height=\"132\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-300x33.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-1024x113.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-150x17.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-768x84.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-720x79.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-520x57.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-320x35.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>Create the training and testing data from the datasets unlike the formal method of using the train_test_split() function from the sklearn.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">X_train = train[0]\r\nY_train = train[1]\r\n\r\nX_test = test[0]\r\nY_test = test[1]\r\n\r\nfor ix in range(X_train.shape[0]):\r\n    X_train[ix] = X_train[ix].split()\r\n\r\nfor ix in range(X_test.shape[0]):\r\n    X_test[ix] = X_test[ix].split()\r\n    \r\nY_train = to_categorical(Y_train)<\/pre>\n<p>Now, let&#8217;s check if our above conversion to categorical labels is done.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">print (X_train[0],Y_train[0])\r\nnp.unique(np.array([len(ix) for ix in X_train]) , return_counts=True)\r\n<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96640\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info.png\" alt=\"prediction info\" width=\"1200\" height=\"213\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info-300x53.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info-1024x182.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info-150x27.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info-768x136.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info-720x128.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info-520x92.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/prediction-info-320x57.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>We use word embeddings in this emoji prediction project to represent the text.<\/p>\n<p>The relationship between the words is represented using word embeddings. This process aims to create a vector with lesser dimensions. An embedding is a low-dimensional space into which high-dimensional vectors can be translated. Machine learning on huge inputs, such as sparse vectors representing words, is made simpler via embeddings.<\/p>\n<p>We use the 6B 50D GloVe vector to build the embedding matrix for the text in our dataset. The file can be downloaded from <a href=\"https:\/\/www.kaggle.com\/watts2\/glove6b50dtxt\">here<\/a>.<\/p>\n<p>Now compute the embedding matrix.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">embeddings_index = {}\r\n\r\n\r\nf = open('.\/glove.6B.50d.txt', encoding=\"utf-8\")\r\nfor line in f:\r\n    values = line.split()\r\n    word = values[0]\r\n    coefs = np.asarray(values[1:], dtype='float32')\r\n    embeddings_index[word] = coefs\r\nf.close()\r\n<\/pre>\n<p>Now, as a part of EDA let&#8217;s check the cosine similarity scores of various words like happy and sad, India and Delhi, France, and Paris.<\/p>\n<p>Observe how these words are similar.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">from scipy import spatial\r\n\r\nspatial.distance.cosine(embeddings_index[\"happy\"],embeddings_index[\"sad\"])\r\n\r\nspatial.distance.cosine(embeddings_index[\"india\"],embeddings_index[\"delhi\"])\r\n\r\nspatial.distance.cosine(embeddings_index[\"france\"],embeddings_index[\"paris\"])\r\n<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96641\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities.png\" alt=\"similarities\" width=\"1200\" height=\"419\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities-300x105.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities-1024x358.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities-150x52.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities-768x268.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities-720x251.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities-520x182.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/similarities-320x112.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>Fill the embedding matrix.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import numpy as np\r\n\r\nembedding_matrix_train = np.zeros((X_train.shape[0], 10, 50))\r\nembedding_matrix_test = np.zeros((X_test.shape[0], 10, 50))\r\n\r\nfor ix in range(X_train.shape[0]):\r\n    for ij in range(len(X_train[ix])):\r\n        embedding_matrix_train[ix][ij] = embeddings_index[X_train[ix][ij].lower()]\r\n        \r\nfor ix in range(X_test.shape[0]):\r\n    for ij in range(len(X_test[ix])):\r\n        embedding_matrix_test[ix][ij] = embeddings_index[X_test[ix][ij].lower()] \r\n<\/pre>\n<p>Let&#8217;s see the shape of the embedding matrix.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96642\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape.png\" alt=\"embeddings shape\" width=\"1200\" height=\"140\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape-300x35.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape-1024x119.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape-150x18.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape-768x90.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape-720x84.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape-520x61.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/embeddings-shape-320x37.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h4>Step 2: Build the Text Classifier for Emoji Prediction<\/h4>\n<p>For this emoji prediction project, we will be using a simple LSTM network.<\/p>\n<p>LSTM stands for Long Short Term Network. Recurrent neural networks are a type of deep neural network used to deal with sequential types of data like audio files, text data, etc.<\/p>\n<p>LSTMs are a variant of Recurrent neural networks that are capable of learning long-term dependencies. LSTM networks work well with time-series data.<\/p>\n<p>&nbsp;<\/p>\n<p>Let&#8217;s build a simple LSTM network using TensorFlow.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">from tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense, Input, Dropout, SimpleRNN, LSTM, Activation\r\n\r\nmodel = Sequential()\r\nmodel.add(LSTM(128, input_shape=(10,50), return_sequences=True))\r\nmodel.add(Dropout(0.5))\r\nmodel.add(LSTM(128, return_sequences=False))\r\nmodel.add(Dropout(0.5))\r\nmodel.add(Dense(5))\r\nmodel.add(Activation('softmax'))<\/pre>\n<p>Let&#8217;s look at the summary of our model.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model.summary()\r\n<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96643\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary.png\" alt=\"emoji prediction model summary\" width=\"1200\" height=\"495\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary-300x124.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary-1024x422.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary-150x62.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary-768x317.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary-720x297.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary-520x215.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-model-summary-320x132.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h4>Step 3: Train and evaluate the ml model<\/h4>\n<p>Now compile and fit our model against the embedding matrix we have computed using the training data. The categorical crossentropy is used as the loss function and Adam is used as the optimizer function. For metrics, accuracy is used.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n\r\nhist = model.fit(embedding_matrix_train,Y_train, epochs = 50, batch_size=32,shuffle=True)<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96644\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs.png\" alt=\"epochs\" width=\"1200\" height=\"423\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs-300x106.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs-1024x361.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs-150x53.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs-768x271.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs-720x254.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs-520x183.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/epochs-320x113.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>We got 100% accuracy on our emoji prediction training set!<\/p>\n<p>Let&#8217;s predict the emoji labels on the testing data.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">pred = model.predict_classes(embedding_matrix_test)\r\n\r\nfloat(sum(pred==Y_test))\/embedding_matrix_test.shape[0]<\/pre>\n<p>Below code prints the text in the test dataset and its predicted emoji by our text classifier.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">for ix in range(embedding_matrix_test.shape[0]):\r\n    \r\n    if pred[ix] != Y_test[ix]:\r\n        print(ix)\r\n        print (test[0][ix],end=\" \")\r\n        print (emoji.emojize(emoji_dict[pred[ix]], use_aliases=True),end=\" \")\r\n        print (emoji.emojize(emoji_dict[Y_test[ix]], use_aliases=True))\r\n<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96645\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output.png\" alt=\"emoji prediction output\" width=\"1200\" height=\"309\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output-300x77.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output-1024x264.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output-150x39.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output-768x198.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output-720x185.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output-520x134.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-output-320x82.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>Let&#8217;s print the confusion matrix.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import itertools\r\n\r\nfrom sklearn.metrics import confusion_matrix\r\nconf_matrix = confusion_matrix(Y_test, pred)\r\n\r\nprint(\"Confusion Matrix\")\r\nprint(conf_matrix)<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96649\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix.png\" alt=\"confusion matrix\" width=\"1200\" height=\"135\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix-300x34.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix-1024x115.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix-150x17.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix-768x86.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix-720x81.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix-520x59.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/confusion-matrix-320x36.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>The confusion matrix for emoji prediction when visualized looks like this.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96650\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot.png\" alt=\"emoji prediction confusion matrix plot\" width=\"1200\" height=\"398\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot-300x100.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot-1024x340.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot-150x50.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot-768x255.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot-720x239.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot-520x172.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/emoji-prediction-confusion-matrix-plot-320x106.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p>Plot the loss and accuracies of our model on the dataset.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import matplot.pyplot as plt\r\nepochs = 50\r\n\r\nplt.style.use(\"ggplot\")\r\nplt.figure()\r\nplt.plot(np.arange(0, epochs), hist.history[\"loss\"], label = \"Train Loss\")\r\nplt.plot(np.arange(0, epochs), hist.history[\"accuracy\"], label = \"Train Acc\")\r\n\r\nplt.title(\"Loss and Accuracy plot\")\r\nplt.xlabel(\"Epoch\")\r\nplt.ylabel(\"Loss \/ Accuracy\")\r\nplt.legend(loc = \"lower left\")\r\nplt.savefig(\"plot.jpg\")\r\n<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-96651\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output.png\" alt=\"plot output\" width=\"1200\" height=\"288\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output.png 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output-300x72.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output-1024x246.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output-150x36.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output-768x184.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output-720x173.png 720w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output-520x125.png 520w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2021\/06\/plot-output-320x77.png 320w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h3>Summary<\/h3>\n<p>In this deep learning project, we built a text classifier that predicts an emoji that suits the given text. We achieve good accuracy in the implementation, although based on requirements we can train it with larger datasets. To predict emojis, we used LSTM as LSTM networks work well with time-series data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Emojis are a wonderful method to express oneself. This deep learning project automatically predicts emojis based on a given phrase. About Emoji Prediction Project In this machine learning project, we predict the emoji from&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":96656,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22216],"tags":[21686,24478,24479,20697],"class_list":["post-96550","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-deep-learning","tag-deep-learning-project","tag-emoji-prediction","tag-emoji-prediction-python","tag-machine-learning-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Emoji Prediction using Deep Learning - DataFlair<\/title>\n<meta name=\"description\" content=\"Emoji prediction using machine learning techniques. 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